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ABSTRACT Conformal predictions transform a measurable, heuristic notion of uncertainty into statistically valid confidence intervals such that, for a future sample, the true class prediction will be included in the conformal prediction set at a predetermined confidence. In a Bayesian perspective, common estimates of uncertainty in multivariate classification, namelyp‐values, only provide the probability that the data fits the presumed class model,P(D|M). Conformal predictions, on the other hand, address the more meaningful probability that a model fits the data,P(M|D). Herein, two methods to perform inductive conformal predictions are investigated—the traditional Split Conformal Prediction that uses an external calibration set and a novel Bagged Conformal Prediction, closely related to Cross Conformal Predictions, that utilizes bagging to calibrate the heuristic notions of uncertainty. Methods for preprocessing the conformal prediction scores to improve performance are discussed and investigated. These conformal prediction strategies are applied to identifying four non‐steroidal anti‐inflammatory drugs (NSAIDs) from hyperspectral Raman imaging data. In addition to assigning meaningful confidence intervals on the model results, we herein demonstrate how conformal predictions can add additional diagnostics for model quality and method stability.
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This content will become publicly available on August 1, 2026
JANET: Joint Adaptive predictioN-region Estimation for Time-series
Abstract Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (JointAdaptive predictioN-regionEstimation forTime-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlledK-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET’s superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.
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- PAR ID:
- 10640536
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Machine Learning
- Volume:
- 114
- Issue:
- 8
- ISSN:
- 0885-6125
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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